Mathematical modeling | Time domain analysis | Classical control theory | Time series models
In control engineering, a state-space representation is a mathematical model of a physical system as a set of input, output and state variables related by first-order differential equations or difference equations. State variables are variables whose values evolve over time in a way that depends on the values they have at any given time and on the externally imposed values of input variables. Output variables’ values depend on the values of the state variables. The "state space" is the Euclidean space in which the variables on the axes are the state variables. The state of the system can be represented as a state vector within that space.To abstract from the number of inputs, outputs and states, these variables are expressed as vectors. If the dynamical system is linear, time-invariant, and finite-dimensional, then the differential and algebraic equations may be written in matrix form.The state-space method is characterized by significant algebraization of general system theory, which makes it possible to use Kronecker vector-matrix structures. The capacity of these structures can be efficiently applied to research systems with modulation or without it. The state-space representation (also known as the "time-domain approach") provides a convenient and compact way to model and analyze systems with multiple inputs and outputs. With inputs and outputs, we would otherwise have to write down Laplace transforms to encode all the information about a system. Unlike the frequency domain approach, the use of the state-space representation is not limited to systems with linear components and zero initial conditions. The state-space model can be applied in subjects such as economics, statistics, computer science and electrical engineering, and neuroscience. In econometrics, for example, state-space models can be used to decompose a time series into trend and cycle, compose individual indicators into a composite index, identify turning points of the business cycle, and estimate GDP using latent and unobserved time series. Many applications rely on the Kalman Filter to produce estimates of the current unknown state variables using their previous observations. (Wikipedia).
State Space Models, Part 1: Creation and Analysis
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Create and analyze state-space models using MATLAB® and Control System Toolbox™. State-space models are commonly used for representing linear time-invariant (LTI) systems.
From playlist Control System Design and Analysis
Introduction to State-Space Equations | State Space, Part 1
Check out the other videos in the series: https://youtube.com/playlist?list=PLn8PRpmsu08podBgFw66-IavqU2SqPg_w Part 2 - Pole placement: https://youtu.be/FXSpHy8LvmY Part 3 - Observability and Controllability: https://youtu.be/BYvTEfNAi38 Part 4 - What Is LQR Optimal Control: https://youtu
From playlist State Space
A Conceptual Approach to Controllability and Observability | State Space, Part 3
Check out the other videos in the series: https://youtube.com/playlist?list=PLn8PRpmsu08podBgFw66-IavqU2SqPg_w Part 1 - The state space equations: https://youtu.be/hpeKrMG-WP0 Part 2 - Pole placement: https://youtu.be/FXSpHy8LvmY Part 4 - What Is LQR Optimal Control: https://youtu.be/E_RD
From playlist State Space
Understanding State Machines, Part 2: Why Use Them?
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Learn the basics of state machines in this MATLAB® Tech Talk by Will Campbell. Watch other videos in this series here: https://bit.ly/3hjmRmu Learn how to use finite sta
From playlist Understanding State Machines
Multivariable system representation 2019-04-24
There are two main ways of representing Multivariable systems - state space and transfer function matrices.
From playlist Multivariable
State Space Representation of Differential Equations
In this video we show how to represent differential equations (either linear or non-linear) in state space form. This is useful as it allows us to combine an arbiter number of higher order differential equations into a single set of first order, coupled, matrix equations. Topics and tim
From playlist Ordinary Differential Equations
Understanding State Machines, Part 1: What Are They?
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Learn the basics of state machines in this MATLAB® Tech Talk by Will Campbell. Watch other videos in this series here: https://bit.ly/3hjmRmu Learn how to use finite sta
From playlist Understanding State Machines
Representation theory: Introduction
This lecture is an introduction to representation theory of finite groups. We define linear and permutation representations, and give some examples for the icosahedral group. We then discuss the problem of writing a representation as a sum of smaller ones, which leads to the concept of irr
From playlist Representation theory
From playlist Fall 2020 Course
State Space to Transfer Function
In this video we show how to transform a linear state space representation of a dynamic system to an equivalent transfer function representation. We will derive the transformation of G(s) = C*(s*I-A)^-1*B+D. We will apply this to an example and show how to use Matlab’s various functions
From playlist Control Theory
Transfer Function to State Space
In this video we show how to transform a transfer function to an equivalent state space representation. We will derive various transformations such as controllable canonical form, modal canonical form, and controller canonical form. We will apply this to an example and show how to use Ma
From playlist Control Theory
Tensionless AdS/CFT (Lecture 2) by Matthias Gaberdiel
PROGRAM KAVLI ASIAN WINTER SCHOOL (KAWS) ON STRINGS, PARTICLES AND COSMOLOGY (ONLINE) ORGANIZERS Francesco Benini (SISSA, Italy), Bartek Czech (Tsinghua University, China), Dongmin Gang (Seoul National University, South Korea), Sungjay Lee (Korea Institute for Advanced Study, South Korea
From playlist Kavli Asian Winter School (KAWS) on Strings, Particles and Cosmology (ONLINE) - 2022
Tensionless AdS/CFT (Lecture 1) by Matthias Gaberdiel
PROGRAM KAVLI ASIAN WINTER SCHOOL (KAWS) ON STRINGS, PARTICLES AND COSMOLOGY (ONLINE) ORGANIZERS Francesco Benini (SISSA, Italy), Bartek Czech (Tsinghua University, China), Dongmin Gang (Seoul National University, South Korea), Sungjay Lee (Korea Institute for Advanced Study, South Korea
From playlist Kavli Asian Winter School (KAWS) on Strings, Particles and Cosmology (ONLINE) - 2022
Katrin Wendland: How do quarter BPS states cease being BPS?
CONFERENCE Recorded during the meeting "Vertex Algebras and Representation Theory" the June 09, 2022 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Luca Récanzone Find this video and other talks given by worldwide mathematicians on CIRM's Audiovi
From playlist Mathematical Physics
Similarity Transformation of a Linear Dynamic System
In this video we discuss how to apply a similarity transformation to a linear dynamic system. If we have a state space representation of a dynamic system, we can change the state vector associated with the system and still maintain the same input/output relationship and eigenvalues. Topi
From playlist Flight Mechanics
[BOURBAKI 2018] 13/01/2018 - 2/4 - Raphaël BEUZART-PLESSIS
Progrès récents sur les conjectures de Gan-Gross-Prasad [d'après Jacquet-Rallis, Waldspurger, W. Zhang, etc.] Les conjectures de Gan-Gross-Prasad ont deux aspects: localement elles décrivent de façon explicite certaines lois de branchements entre représentations de groupes de Lie réels ou
From playlist BOURBAKI - 2018
Understanding State Machines, Part 3: Mealy and Moore Machines
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Learn the basics of state machines in this MATLAB® Tech Talk by Will Campbell. Watch other videos in this series here: https://bit.ly/3hjmRmu Learn how to use finite sta
From playlist Understanding State Machines
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 8 - Model-Based Reinforcement Learning
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Assistant Professor Chelsea Finn, Stanford University http://cs330.stanford.edu/
From playlist Stanford CS330: Deep Multi-Task and Meta Learning